Abstract [en]

Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.

In thesis

Kapsch, Marie-Luise

Stockholm University, Faculty of Science, Department of Meteorology .

2015 (English)Doctoral thesis, comprehensive summary (Other academic)

Abstract [en]

The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of the solar radiation than the underlying ocean. Hence, they are important for the global energy budget, and changes in the sea-ice cover can have a large impact on the Arctic climate and beyond. In the past 36 years the ice cover reduced significantly. The largest decline is observed in September, with a rate of more than 12% per decade. The negative trend is accompanied by large inter-annual sea-ice variability: in September the sea-ice extent varies by up to 27% between years. The processes controlling the large variability are not well understood. In this thesis the atmospheric contribution to the inter-annual sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice cover and sea-ice melt. Atmospheric reanalysis data are used to identify key processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found that in years with a very low September sea-ice extent more heat and moisture is transported in spring into the area that shows the largest ice variability. The increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies of water vapor and clouds. These alter the amount of downward radiation at the surface: positive cloud anomalies allow for more longwave radiation and less shortwave radiation. In spring, when the solar inclination is small, positive cloud anomalies result in an increased surface warming and an earlier seasonal melt onset. This reduces the ice cover early in the season and allows for an increased absorption of solar radiation by the surface during summer, which further accelerates the ice melt. The modeling experiments indicate that cloud anomalies of similar magnitude during other seasons than spring would likely not result in below-average September sea ice. Based on these results a simple statistical sea-ice prediction model is designed, that only takes into account the downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the importance of the spring atmosphere for the annual sea-ice evolution.